The Clinical Utility of Vestibular-Evoked Myogenic Potentials in the Diagnosis of Ménière’s Disease
Bibliographic record
Abstract
Ménière's disease (MD) is a condition that has been proposed over 150 years ago, which involves audiological and vestibular manifestations, such as aural fullness, tinnitus, vertigo, and fluctuating hearing thresholds. Over the past few years, many researchers have assessed different techniques to help diagnose this pathology. Vestibular-evoked myogenic potential (VEMP) is an electrophysiological method assessing the saccule (cVEMP) and the utricule (oVEMP). Its clinical utility in the diagnosis of multiple pathologies, such as superior canal dehiscence, has made this tool a common method used in otologic clinics. The main objective of the present review is to determine the current state of knowledge of the VEMP in the identification of MD, such as the type of stimuli, the frequency tuning, and the interaural asymmetry ratio of the cVEMP and the oVEMP. Results show that the type of stimulation, the frequency sensitivity shift and the interaural asymmetry ratio (IAR) could be useful tool to diagnose and describe the evolution of MD. It is, however, important to emphasize that further studies are needed to confirm the utility of VEMP in the identification of MD in its early stage, using either bone-conduction vibration or air-conduction stimulation, which is of clinical importance when it comes to early intervention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".